How to Share Data and Promote a Rapid Learning Health Medicine?

  • Ruud van Stiphout
  • Timo M. Deist
  • Sean Walsh
  • Johan van Soest
  • Arthur Jochems
  • Erik Roelofs
  • Andre Dekker
  • Philippe Lambin
Chapter

Abstract

The current increasing amount of digitalized medical data in healthcare demands for solutions to store, share, mine, and analyze these data. Today, medical knowledge and evidence is based on outdated data. Tomorrow we aim to have a rapid learning healthcare (RLHC) system in which evidence can be generated instantly, based on the most recent data available. The development of this system requires dedication and support of healthcare providers, politicians, and patients on many levels. The aims of this system are improvement of healthcare quality and support in clinical decision making. Full integration of data handling systems within the clinic and between institutes is inevitable in the near future.

Notes

Acknowledgments

The authors acknowledge financial support from the QuIC-ConCePT project, which is partly funded by EFPIA companies and the Innovative Medicine Initiative Joint Undertaking (IMI JU) under Grant Agreement No. 115151. This research is also supported by the Dutch Technology Foundation STW (grant no. 10696 DuCAT), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. They also acknowledge financial support from EU 7th Framework Programme (EURECA, ARTFORCE – no. 257144, REQUITE – no. 601826), Kankeronderzoekfonds Limburg from the Health Foundation Limburg, Alpe d’HuZes-KWF (DESIGN), and the Dutch Cancer Society (KWF MAC 2013-6425, KWF MAC 2013-6089, KWF 2015-7635).

References

  1. 1.
    Abernethy AP, Etheredge LM, Ganz PA et al (2010) Rapid-learning system for cancer care. J Clin Oncol 28(27):4268–4274CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Chan KS, Fowles JB, Weiner JP (2010) Electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev 67(5):503–527CrossRefPubMedGoogle Scholar
  3. 3.
    Collins GS, Reitsma JB, Altman DG et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 162(1):55–64CrossRefPubMedGoogle Scholar
  4. 4.
    Dehing-Oberije C, Aerts H, Yu S et al (2011) Development and Validation of a Prognostic Model Using Blood Biomarker Information for Prediction of Survival of Non–Small-Cell Lung Cancer Patients Treated With Combined Chemotherapy and Radiation or Radiotherapy Alone (NCT00181519, NCT00573040, and NCT00572325).” International Journal of Radiation Oncology* Biology* Physics 81(2):360–368Google Scholar
  5. 5.
    Dekker A, Gulliford S, Ebert M et al (2013) Future radiotherapy practice will be based on evidence from retrospective interrogation of linked clinical data sources rather than prospective randomized controlled clinical trials. Med Phys 41(3):3Google Scholar
  6. 6.
    Dekker A, Vinod S, Holloway L et al (2014) Rapid learning in practice: a lung cancer survival decision support system in routine patient care data. Radiother Oncol 113:47–53CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Iasonos A, Schrag D, Raj GV et al (2008) How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 26(8):1364–1370CrossRefPubMedGoogle Scholar
  8. 8.
    Lambin P, Petit SF, Aerts HJ et al (2010) The ESTRO Breur Lecture. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer. Radiother Oncol 96(2):145–152CrossRefPubMedGoogle Scholar
  9. 9.
    Lambin P, Roelofs E, Reymen B et al (2013a) Rapid learning health care in oncology’ – an approach towards decision support systems enabling customised radiotherapy. Radiother Oncol 109(1):159–164CrossRefPubMedGoogle Scholar
  10. 10.
    Lambin P, van Stiphout RG, Starmans MH et al (2013b) Predicting outcomes in radiation oncology – multifactorial decision support systems. Nat Rev Clin Oncol 10(1):27–40CrossRefPubMedGoogle Scholar
  11. 11.
    Lambin P et al (2015) Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine. Acta Oncologica 54(9):1289–1300Google Scholar
  12. 12.
    Meldolesi E, van Soest J, Dinapoli N et al (2014) An umbrella protocol for standardized data collection (SDC) in rectal cancer. A prospective uniform naming and procedure convention to support personalized medicine. Radiother Oncol 112:59–62CrossRefPubMedGoogle Scholar
  13. 13.
    Roelofs E et al (2010) Design of and technical challenges involved in a framework for multicentric radiotherapy treatment planning studies. Radiother Oncol 97(3):567–571CrossRefPubMedGoogle Scholar
  14. 14.
    Starmans MH, Zips D, Wouters BG et al (2009) The use of a comprehensive tumour xenograft dataset to validate gene signatures relevant for radiation response. Oral Oncol 92:417–422Google Scholar
  15. 15.
    Valentini V, van Stiphout RGPM, Lammering G et al (2011) Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials. J Clin OncolGoogle Scholar
  16. 16.
    van Stiphout RG, Lammering G, Buijsen J et al (2011) Development and external validation of a predictive model for pathological complete response of rectal cancer patients including sequential PET-CT imaging. Radiother Oncol 98(1):126–133CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2018

Authors and Affiliations

  • Ruud van Stiphout
    • 1
  • Timo M. Deist
    • 2
  • Sean Walsh
    • 2
  • Johan van Soest
    • 2
  • Arthur Jochems
    • 2
  • Erik Roelofs
    • 2
  • Andre Dekker
    • 2
  • Philippe Lambin
    • 2
  1. 1.Ludwig Cancer ResearchUniversity of OxfordOxfordUnited Kingdom
  2. 2.Department of Radiation Oncology (MAASTRO)GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+MaastrichtThe Netherlands

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